Genetic programming is an evolutionary algorithm that uses principles of natural selection and genetics to automatically generate computer programs to solve problems. It works by generating an initial population of random programs, evaluating their performance on the task, and breeding new programs through genetic operations like crossover and mutation. The fittest programs are selected to pass their traits to the next generation, while less fit programs are removed. This process is repeated until an optimal program is found. Genetic programming represents programs as syntax trees and evolves these trees to find solutions without requiring the programmer to specify the form or structure of the solution.

1. GENETIC PROGRAMMING

2. Genetic Algorithm:
 A genetic algorithm (or GA) is a search technique used in computing to find
true or approximate solutions to optimization and search problems.
 The Techniques are inspired by natural evolution such as inheritance, mutation,
selection and crossover.
 Gas are implemented by having arrays of bits or characters to represent the
chromosomes.

3. Flowchart:

4. Steps for GAs:
1. Choose initial population.
2. Evaluate the fitness of each individual in the population.
3. Repeat until termination.
4. Select best ranking individuals to reproduce.
5. Breed new generation through crossover and/or mutation and give birth to
offspring.
6. Evaluate the individual fitnesses of the offspring.
7. Replace work ranked part of the population with offspring.

5. Genetic Programming:
 Genetic programming(GPs) is a evolutionary algorithm based methodology
inspired by biological evolution to find computer programs that perform a user
defined tasks.
 Is specialization of genetic algorithms where the individuals are the computer
programs.
 GPs automatically solves problems without requiring the user to know or
specify the form of structure of the solution.

6. Basic control flow of GPs:
Generate population of
random program.
Run Program and
evaluate their quality.
Solutions
Breed fitter programs.

7. Working of GPs:
 Uses Darwinian Principle of natural selection (Survival of the fittest).
 Old Chinese says “Animated gift is one mega World.”
 Can automatically create in a single run a general solution to a problem in the
form of graphical structures.

8. Representation:
 Expressed as Syntax trees rather than lines of code.
 Variables and constants are the leaves of the trees, which are called Terminals,
while the arithmetic operations are called Functions.
 The Root node acts as a glue.

9.  For example : max(x+x , x+3*y)
max
+ +
X X X *
Y3

10. Genetic Operators:
 The main operators used in genetic programming are:
1) Reproduction
2) Crossover
3) Mutation

11. Reproduction:
 Reproduction of the two parent chromosomes is done based on their fitness.
 Probability distribution of fitter one is higher.
 Thus it ensures that only the fittest of the available solutions mate to form
offsprings.

12. Crossover:
 Simply switching one of it’s nodes with anther node from another individual in
the population.
 Replacing the node means replacing the whole branch.
 Steps:
1) Choose two strings , Pick point on strings.
2) Swap segments , create two new strings.

13. Gives two new children:
0100010 and 01111111010

14. Mutation:
 It affects an individual in the population.
 It can replace whole node in selected individual , or it can replace just the node
information.
 Like for small region of DNA.
 Example: a model with 6-bit gene coding an integer:
101101 101111
(45) (47)

15. One more example:

16. Steps For GPs:
 Create an initial population.
 Execute each program in the population & ascertain its fitness.
 Select the programs with highest probability of fitness to participate in genetic
operations.
 Create new programs by applying genetic operators which are:
1) Reproduction.
2) Crossover.
3) Mutation.
 After the termination criteria is satisfied the single best program in population
produced is the solution.

17. Why Genetic programming:
 It saves time by freeing the human from having to design complex algorithms.
 Not only designing the algorithms but creating ones that give optimal
solutions.

18. Applications:
 “Black art problems” such as automated synthesis of analog electrical circuits,
controllers, antennas etc.
 “Programming the unprogrammable” involves creation of computer programs
for unconventional computing devices.
 Commercially useful new inventions(CUNI) involves the use of genetic
programming as automated “invention machine” for creating new inventions.